from typing import Callable

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
from torch.utils.hooks import RemovableHandle


# ResNet
class Residual(nn.Module):
    def __init__(self,input_channels,num_channels,use_1x1conv=False,strides=1):
        super().__init__()
        self.conv1 = nn.Conv2d(input_channels,num_channels,kernel_size=3,padding=1,stride=strides)
        self.conv2 = nn.Conv2d(num_channels,num_channels,kernel_size=3,padding=1)
        if use_1x1conv:
            self.conv3 = nn.Conv2d(input_channels,num_channels,kernel_size=1,stride=strides)
        else:
            self.conv3 = None
        self.bn1 = nn.BatchNorm2d(num_channels)
        self.bn2 = nn.BatchNorm2d(num_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self,X):
        Y = F.relu(self.bn1(self.conv1(X)))
        Y = self.bn2(self.conv2(Y))
        if self.conv3 != None:
            X = self.conv3(X)
        Y += X
        return F.relu(Y)

b1 = nn.Sequential(nn.Conv2d(1,64,kernel_size=7,stride=2,padding=1),
                   nn.BatchNorm2d(64),nn.ReLU(),nn.MaxPool2d(kernel_size=3,stride=2,padding=1))
def resnet_block(input_channels,num_channels,num_residuals,first_block=False):
    blk = []
    for i in range (num_residuals):
        if i==0 and not first_block:
            blk.append(
                Residual(input_channels,num_channels,use_1x1conv=True,strides=2))
        else:
            blk.append(Residual(num_channels,num_channels))
        return blk

b2 = nn.Sequential(*resnet_block(64,64,2,first_block=True))
b3 = nn.Sequential(*resnet_block(64,128,2))
b4 = nn.Sequential(*resnet_block(128,256,2))
b5 = nn.Sequential(*resnet_block(256,512,2))

net = nn.Sequential(b1,b2,b3,b4,b5,nn.AdaptiveAvgPool2d((1,1)),nn.Flatten(),nn.Linear(512,10))

X = torch.rand(size=(1,1,224,224))
for layer in net:
    X = layer(X)
    print(f"{layer.__class__.__name__},output shape:\t{X.shape}")



